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<p>Vehicular Ad Hoc Networks (VANETs) are a key component of Intelligent Transportation Systems (ITS), enabling vehicle-to-everything (V2X) communication to improve traffic efficiency and road safety. However, maintaining strong network performance and user privacy requires robust security mechanisms. Unlike existing blockchain-VANET solutions that address isolated aspects, our framework uniquely integrates Physical Unclonable Functions (PUFs) for lightweight authentication, an Enhanced Quantified role-based Practical Byzantine Fault Tolerance (EQPBFT) consensus mechanism for efficient and secure block creation, and a Deep Neural Network (DNN)-based intrusion detection system into a unified trust management pipeline. The EQPBFT algorithm improves network resilience by efficiently identifying and mitigating malicious nodes during block creation. In addition, a DNN-based intrusion detection system enhances threat classification and strengthens attack detection accuracy. Simulation results demonstrate reduced computation time (up to 25% lower than PBFT), faster block creation (5.2 ms vs. 6.6 ms for 200 nodes), and higher intrusion detection accuracy (95%) compared to baseline models. These quantitative outcomes validate the robustness and efficiency of the proposed system.</p>

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Blockchain driven trust management for intelligent transportation systems in VANETs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月12日

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  • Hariharasudhan V,
  • Vetrivelan P,
  • Elizabeth Chang

摘要

Vehicular Ad Hoc Networks (VANETs) are a key component of Intelligent Transportation Systems (ITS), enabling vehicle-to-everything (V2X) communication to improve traffic efficiency and road safety. However, maintaining strong network performance and user privacy requires robust security mechanisms. Unlike existing blockchain-VANET solutions that address isolated aspects, our framework uniquely integrates Physical Unclonable Functions (PUFs) for lightweight authentication, an Enhanced Quantified role-based Practical Byzantine Fault Tolerance (EQPBFT) consensus mechanism for efficient and secure block creation, and a Deep Neural Network (DNN)-based intrusion detection system into a unified trust management pipeline. The EQPBFT algorithm improves network resilience by efficiently identifying and mitigating malicious nodes during block creation. In addition, a DNN-based intrusion detection system enhances threat classification and strengthens attack detection accuracy. Simulation results demonstrate reduced computation time (up to 25% lower than PBFT), faster block creation (5.2 ms vs. 6.6 ms for 200 nodes), and higher intrusion detection accuracy (95%) compared to baseline models. These quantitative outcomes validate the robustness and efficiency of the proposed system.